Implementing demand response 2.0: Progress toward full potential in the United States

Demand response can be an excellent flexible resource and can cost-effectively support a grid with high penetrations of variable renewable generation. Unfortunately, this potential has not yet been widely exploited, due to a number of obstacles at the wholesale and distribution levels. This article reviews those barriers and the prospects for better uptake of demand response.     

Decarbonization of electricity requires market-based demand flexibility

To effectively decarbonize the electric sector, utilities will need to address the growing load shape challenges driven by the variability of many renewable resources. Behind-the-meter solutions, such as energy efficiency, demand response, electrification and storage, will play an important role in grid stability, but only if they can deliver changes in demand that meet the time and locational needs of the grid. This article will discuss how smart meter interval data, combined with open source methods and software, provide transparent measurement of savings load shapes (resource curves) that enable the integration of demand flexibility into energy, capacity and carbon markets, and as a transmission and distribution resource. This allows utilities to procure demand flexibility in the same way they procure other resources by leveraging a price signal and pay-for-performance to drive innovation and attract private investment.     

Unlocking the value of flexibility of behind-the-meter prosumers: An overview of mechanisms to esteemed trends

Behind-the-meter (BTM) resources is being recognized as a viable solution to offer grid services including flexibility procurement which is required for volatile renewable power systems. This paper brings an overview of current and esteemed frameworks and respective challenges revolving around BTM flexibility notion and mechanisms. To begin with, we review grid architectures, e.g., microgrids and virtual power plants, capable of accommodating BTM flexibility and desirable flexibility market designs, including peer-to-peer trading. The role of machine learning initiatives, including reinforcement learning and probabilistic forecasting, in designing reliable energy management systems is extensively deliberated. Last but not least, supplementary discussions in making this concept a reality, which can be regarded as future research, are given.     

基于PC的柔性智能测控系统集成平台研究(PC-FISP)

本文将柔性控制、智能控制的思想与传统测控系统结合起来，提出了基于PC的柔性智能测控系统集成平台的基本结构，对开发平台涉及的关键问题进行了讨论，最后给出了一种基于PC的柔性智能测控系统集成平台的模型。     

基于元数据的柔性信息系统建模与实现

介绍数据柔性、数据柔性分析等相关概念。通过引入元数据,提出并建立数据柔性的层次模型和柔性数据的访问模型,以解决用户数据结构如何适应转化的关键问题。在数据柔性分析与建模中,提出以元数据为核心的软件设计方法,并将该方法和所建立的数据柔性理论应用于柔性软件信息系统的开发和软件工程实践中。     

Association-Based Active Access Control models with balanced scalability and flexibility

In existing Active Access Control (AAC) models, the scalability and flexibility of security policy specification should be well balanced, especially: (1) authorizations to plenty of tasks should be simplified; (2) team workflows should be enabled; (3) fine-grained constraints should be enforced. To address this issue, a family of Association-Based Active Access Control (ABAAC) models is proposed. In the minimal model ABAAC₀, users are assigned to roles while permissions are assigned to task-role associations. In a workflow case, to execute such an association some users assigned to its component role will be allocated. The association's assigned permissions can be performed by them during the task is running in the case. In ABAAC₁, a generalized association is employed to extract common authorizations from multiple associations. In ABAAC₂, a fine-grained separation of duty (SoD) is enforced among associations. In the maximal model ABAAC₃, all these features are integrated, and similar constraints can be specified more concisely. Using a software workflow, case validation is performed. Comparison with a representative association based AAC model and the most scalable AAC model so far indicates that: (1) enough scalability is achieved; (2) without decomposition of a task, different permissions can be authorized to multiple roles in it; (3) separation of more fine-grained duties than roles and tasks can be enforced.     

Agent-based management of support systems for distributed brainstorming

In this paper, we describe the design and the management of an agent-based system that supports distributed brainstorming activities. The support system is a highly coordinated IoT application composed of many locally installed interface devices, multimedia communication functions, and cloud functions that process application logic and store meeting data. The system is designed to support a variety of brainstorming sessions, so its functionalities must be modifiable and enable the system to be adapted to different environments and user requirements without any loss of performance. System accessibility should be also ensured from any location for any user. These constraints require a flexible and usable support system.We further discuss the aspects of flexibility and usability that are important in a support system for distributed brainstorming, from which we propose a conceptual schema for flexible and usable support systems. To realize this schema, we present a resource-oriented architecture that can modify the brainstorming support system’s structure and functions. Flexibility is achieved thanks to an agent-based system that manages resources and operates on them according to users’ requests.We also describe the system architecture, which is organized around a set of channels dedicated to different services proposed to the users. We present in detail a video channel that ensures user awareness during synchronized activities. We then conduct several experiments verifying the usability of important channels in the architecture and present the results of these experiments.Finally, we discuss experimental scenarios that show how the system owes its adaptability to management based on an agent organization that supports distributed brainstorming and other activities.     

Modular architecture of the microfactories for automatic micro-assembly

The construction of a new generation of MEMS which includes micro-assembly steps in the current microfabrication process is a big challenge. It is necessary to develop new production means named micromanufacturing systems in order to perform these new assembly steps. The classical approach called “top-down” which consists in a functional analysis and a definition of the tasks sequences is insufficient for micromanufacturing systems. Indeed, the technical and physical constraints of the microworld (e.g. the adhesion phenomenon) must be taken into account in order to design reliable micromanufacturing systems. A new method of designing micromanufacturing systems is presented in this paper. Our approach combines the general “top-down” approach with a “bottom-up” approach which takes into account technical constraints. The method enables to build a modular architecture for micromanufacturing systems. In order to obtain this modular architecture, we have devised an original identification technique of modules and an association technique of modules. This work has been used to design the controller of an experimental robotic micro-assembly station.     

认知灵活性理论指导下的网络教学交互设计

本文首先介绍了认知灵活性理论的涵义及其关于学习和教学的观点;探讨了认知灵活性理论对开展网络教学交互设计的启发性意义;从"人-机"交互和"人-人"交互两个方面,探究了认知灵活性理论柏导下的网络教学系统中的交互设计。     

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.